Propensity versus Lifetime Value — which and when?

Two common metrics used in almost every sophisticated analytics effort are Customer Lifetime Value (CLV) and Propensity to Buy.CLV is often seen as the golden child of marketing analytics, used by many businesses and tracked meticulously. Propensity to Buy is its lesser-known cousin. Here, we make an argument for why Propensity to Buy might be better, and when to use each.
To begin, let’s start with definitions:

Customer Lifetime Value is used to gauge how much a customer is likely to spend throughout their entire period of being a customer of your business. It is a combination of money they have already spent and in more sophisticated cases, the amount they are expected to spend in the future.

Propensity to Buy is fully predictive and not revenue-based. It estimates the probability that a customer is likely to buy again in the future. It could be used to predict future interest in a specific product, or likelihood to buy anything from the company as a whole.

Lifetime Value comes with the benefit of giving an actual revenue estimate for every single customer. More importantly, it is easy to understand: everyone within a company will understand the benefits of revenue from a customer, and why increasing revenue per customer is important to the business. Propensity to Buy is more complex: it requires a basic understanding of probabilities, and only looks to the future.

While more difficult to comprehend, Propensity to Buy comes with a benefit ill-afforded by Lifetime Value: it helps you plan exactly which customers you should be reaching out to. Specifically, Lifetime Value will allow you to rank customers, but you are less certain of where their value is coming from, and whether or not it is expected to continue. Take a customer with a value estimate of $1,000. It is unclear whether this is money already spent, or whether this is an estimate for future spending. As such, deciding what to do next is much more difficult.

Propensity to Buy is solely foreward looking — it tells you how likely the customer is to buy in the future, taking into account earlier spending habits. The benefit of a foreward looking measure is that you can preempt what every customer is likely to do. Customers with high propensities need no additional encouragement to purchase, while those with low propensities need aggressive outbound marketing campaigns. Those in the middle might need a discount code or “thank you” e-mail, but investment to convert them should remain relatively low.

So which should you use? If planning for future marketing campaigns, and in particular if your products have similar prices, then Propensity to Buy is what we recommend. It will help rank your customers in terms of outreach potential, and will make it easier for your sales team to track, respond to, and convert great leads.

Of course, combining Customer Lifetime Value and Propensity to Buy is the ideal approach, as you can have even more nuanced recommendations based on both. The challenge with combining them is time spent building the models, ensuring they are accurate, and that your team knows how to use them properly. Of course, this is a great reason to use an automated customer analytics solution.

We’d love to hear your thoughts. Do you use CLV or propensity to buy? How have you seen the process work?

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Wojciech Gryc is the CEO of Canopy Labs. Prior to Canopy Labs, Wojciech was a consultant with McKinsey & Co. and a researcher at IBM Research. Wojciech is a Rhodes Scholar and Loran Scholar.

2 Comments

Greg on January 17, 2013 at 4:19 pm

I think the best approach would be to use both. CLV + Propensity to buy = a sort of expected value for purchasing. Seems like it gets the best results for us. Only challenge is scaling it to lots of products (ie tens of thousands) but if you’ve only got several or a few dozen, you should be good.